difference between athena and redshift spectrum

Difference between athena and redshift spectrum series#

It uses a distributed, MPP, and shared nothing architecture. This pattern is powerful because it uses the highly optimized and scalable data storage and compute power of MPP architecture.Īmazon Redshift is a fully managed data warehouse service on AWS. The second diagram is ELT, in which the data transformation engine is built into the data warehouse for relational and SQL workloads. This pattern allows you to select your preferred tools for data transformations. In the following diagram, the first represents ETL, in which data transformation is performed outside of the data warehouse with tools such as Apache Spark or Apache Hive on Amazon EMR or AWS Glue.

The second pattern is ELT, which loads the data into the data warehouse and uses the familiar SQL semantics and power of the Massively Parallel Processing (MPP) architecture to perform the transformations within the data warehouse. The first pattern is ETL, which transforms the data before it is loaded into the data warehouse. This also determines the set of tools used to ingest and transform the data, along with the underlying data structures, queries, and optimization engines used to analyze the data. The primary difference between the two patterns is the point in the data-processing pipeline at which transformations happen. There are two common design patterns when moving data from source systems to a data warehouse. Part 2 of this series, ETL and ELT design patterns for modern data architecture using Amazon Redshift: Part 2, shows a step-by-step walkthrough to get started using Amazon Redshift for your ETL and ELT use cases. You also learn about related use cases for some key Amazon Redshift features such as Amazon Redshift Spectrum, Concurrency Scaling, and recent support for data lake export.

Difference between athena and redshift spectrum series#

Part 1 of this multi-post series discusses design best practices for building scalable ETL (extract, transform, load) and ELT (extract, load, transform) data processing pipelines using both primary and short-lived Amazon Redshift clusters. New: Read Amazon Redshift continues its price-performance leadership to learn what analytic workload trends we’re seeing from Amazon Redshift customers, new capabilities we have launched to improve Redshift’s price-performance, and the results from the latest benchmarks.